Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …

Semi-supervised multi-modal learning has attracted much attention due to the expense and
scarcity of data labels, especially in disease diagnosis field. Most existing methods follow …

Semi-supervised learning is an effective method to study how to use both labeled data and
unlabeled data to improve the performance of the classifier, which has become the hot field …

The goal of balanced clustering is partitioning data into distinct groups of equal size.
Previous studies have attempted to address this problem by designing balanced …

Unsupervised multi-view bipartite graph clustering (MVBGC) is a fast-growing research, due
to promising scalability in large-scale tasks. Although many variants are proposed by …

Graph-based semi-supervised dimension reduction can use the inherent graph structure of
samples to propagate label information, and has become a hot research field in machine …

Graph Neural Networks (GNNs) are a popular machine learning framework for solving
various graph processing applications. This framework exploits both the graph topology and …

Recently, graph-based semi-supervised learning (GSSL) has received much attention. On
the other hand, less attention has been paid to the problem of large-scale GSSL for inductive …

Graph-based semisupervised learning can explore the graph topology information behind
the samples, becoming one of the most attractive research areas in machine learning in …

Graph-based semi-supervised learning (GSSL) has received much attention recently.
Despite some progress made in this area by some recent methods, some limitations still …